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Objective: To study nurses' perceptions of the quality of communication with the families of patients admitted to an intensive care unit (ICU)., Method: We performed a cross-sectional, descriptive study among nurses at the ICU of a University Hospital. An anonymous survey was conducted utilizing the modified Nurse Activities for Communicating with Families questionnaire, which measures nurse activities for communicating with patients' relatives., Results: We interviewed 70 nurses, representing 87.5% of the nursing staff. Forty-seven percent (n=33) sometimes spoke to the family about the patient's illness and treatment and 61% (n=43) spoke about the patient's medical equipment and treatment. Only 17% (n=12) usually asked the relatives about the information provided by the physician. Twenty percent (n=14) never spoke to relatives about their feelings. Twenty-six percent (n=18) never spoke to the family about the patient's life. Older nursing staff were more likely to ask the family about the patient's life (p=0.044). Only 48% (n=34) sometimes spoke to the family about changes in the patient's care plan, and those employed for a longer time at the unit were less likely to speak about this subject (p=0.028)., Conclusions: We found a widespread absence of nurse activities for communicating with the families of admitted patients. Older staff spoke more frequently about patient's lives and those working for longer in the unit were less likely to inform relatives about the care plan.

Objective: To compare the degree of severity of burnout syndrome among the healthcare personnel of an intensive care unit (ICU) and admission wards., Method: We performed a cross-sectional, descriptive study with nurses and nurses' aides in the ICU and admission wards of the Hospital Universitario Insular de Gran Canaria (Spain). An anonymous survey was performed through the 22-item Maslach Burnout Inventory, which measures the three dimensions of burnout syndrome: emotional exhaustion, depersonalization and personal accomplishment., Results: We interviewed 92 nurses (61% from the ICU and 39% from admission wards) and 80 nurses' aides (51% from the ICU and 49% from admission wards). In all groups, a moderate degree of emotional exhaustion was found, which was more severe in nurses' aides working on admission wards than in those working in the ICU (22.26, SD=7.47 vs 26.51, SD=7.12, p=0.011). High levels of depersonalization and emotional exhaustion were found in all groups, with no significant difference among staff working in the ICU or admission wards., Conclusions: No significant differences were found in the perception of burnout between staff in the ICU or in admission wards. The degree of emotional exhaustion was moderate, while degrees of depersonalization and lack of personal accomplishment were high.

From the moment a patient is admitted to the hospital, monitoring begins, and specific information is collected. The continuous flow of parameters, including clinical and analytical data, serves as a significant source of information. However, there are situations in which not all values from medical tests can be obtained. This paper aims to predict the medical test values of COVID-19 patients in the intensive care unit (ICU). By retrieving the missing medical test values, the model provides healthcare professionals with an additional tool and more information with which to combat COVID-19. The proposed approach utilizes a customizable deep learning model. Three types of neural networks, namely Multilayer Perceptron (MLP), Long/Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), are employed. The parameters of these neural networks are configured to determine the model that delivers the optimal performance. Evaluation of the model's performance is conducted using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The application of the proposed model achieves predictions of the retrieved medical test values, resulting in RMSE = 7.237, MAPE = 5.572, and MAE = 4.791. Moreover, the article explores various scenarios in which the model exhibits higher accuracy. This model can be adapted and utilized in the diagnosis of future infectious diseases that share characteristics with Coronavirus Disease 2019 (COVID-19). [ABSTRACT FROM AUTHOR]

Objective: To compare outcomes of critically ill patients transferred from another island compared to those patients with direct admission from Emergency Department to intensive care unit (ICU). Patients and Methods: Retrospective study of prospectively collected data during 8 years. The population studied was all critical adult patients transferred from another island to our hospital and those directly admitted from the Emergency Department. Variables were age, sex, clinical diagnosis (coronary, medical, surgical, or trauma), acute physiology and chronic health evaluation (APACHE) II score at admission, ICU days of stay, days of mechanical ventilation and ICU mortality. Results: During the period of study, 3,115 patients coming from Emergency Department (Group 1) were admitted to our ICU and 138 were transferred from another island (Group 2). No significant statistically differences were found between both groups neither age, sex, APACHE II, ICU days, days of mechanical ventilation, and mortality rate (17.5% versus 20.3%, P = 0.43). The multivariate analysis showed that age, APACHE II score, ICU days of stay, type of patient, and days of mechanical ventilation were independent variables associated with mortality. Conclusions: No differences were found in the global prognosis of the admitted patients transferred from another island compared to those who were admitted directly from the Emergency Department. There is no impact on mortality in transferring a patient in our study population. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article related to factors affecting survival rate of patients that were associated with prolonged admission to the intensive care unit (ICU) in the previous issue.